https://carlos-mendez.org/books/BooksWowchemy (https://wowchemy.com)en-us© 2018–2026 Carlos Mendez. All rights reserved.https://carlos-mendez.org/media/icon_huedfae549300b4ca5d201a9bd09a3ecd5_79625_512x512_fill_lanczos_center_3.pnghttps://carlos-mendez.org/books/https://carlos-mendez.org/books/ccm/Sun, 17 May 2026 00:00:00 +0000https://carlos-mendez.org/books/ccm/<h2 id="welcome-to-comparative-causal-metrics-work-in-progress">Welcome to Comparative Causal Metrics! (Work in Progress)</h2> <p><em>An Introduction to Regional Impact Evaluation</em></p> <p>An introduction to <strong>regional impact evaluation</strong> using modern causal-inference methods implemented in R and rendered with Quarto. The resource covers quasi-experimental techniques for evaluating policy effects and interventions on regional outcomes, with worked examples and publicly available data for full reproducibility.</p> <p>This work in progress book features:</p> <ul> <li><strong>A comparative tour of methods</strong> — From interrupted time series and difference-in-differences to synthetic control, Bayesian structural time series, and modern panel-data estimators, all with a regional comparative focus.</li> <li><strong>R + Quarto Notebooks</strong> — Reproducible chapters with collapsible code, ready to render locally or extend with your own data.</li> </ul> <p>The book is organized in two parts:</p> <ul> <li><strong>Part I — Single treated unit (Chapters 1–9)</strong> builds intuition with one running case study: California&rsquo;s 1989 Proposition 99 cigarette tax.</li> <li><strong>Part II — Staggered adoption (Chapters 10–12)</strong> moves to settings where many units adopt a policy at different times, using a Callaway–Sant&rsquo;Anna minimum-wage county panel.</li> </ul> <h2 id="chapters">Chapters</h2> <p><strong>Part I — Single treated unit</strong></p> <ol> <li><a href="https://quarcs-lab.github.io/ccm/01-introduction.html" target="_blank" rel="noopener">Introduction</a></li> <li><a href="https://quarcs-lab.github.io/ccm/02-interrupted-time-series.html" target="_blank" rel="noopener">Interrupted Time Series</a></li> <li><a href="https://quarcs-lab.github.io/ccm/03-basic-diff-in-diff.html" target="_blank" rel="noopener">Basic Differences-in-Differences</a></li> <li><a href="https://quarcs-lab.github.io/ccm/04-classical-synthetic-control.html" target="_blank" rel="noopener">Classical Synthetic Control</a></li> <li><a href="https://quarcs-lab.github.io/ccm/05-augmented-synthetic-control.html" target="_blank" rel="noopener">Augmented Synthetic Control</a></li> <li><a href="https://quarcs-lab.github.io/ccm/06-synthetic-did.html" target="_blank" rel="noopener">Synthetic Difference-in-Differences</a></li> <li><a href="https://quarcs-lab.github.io/ccm/07-structural-bayesian-ts.html" target="_blank" rel="noopener">Structural Bayesian Time Series</a></li> <li><a href="https://quarcs-lab.github.io/ccm/08-synthetic-control-prediction-intervals.html" target="_blank" rel="noopener">Synthetic Control with Prediction Intervals</a></li> <li><a href="https://quarcs-lab.github.io/ccm/09-bayesian-spatial-sc.html" target="_blank" rel="noopener">Bayesian Spatial Synthetic Control</a></li> </ol> <p><strong>Part II — Staggered adoption</strong></p> <ol start="10"> <li><a href="https://quarcs-lab.github.io/ccm/10-staggered-did.html" target="_blank" rel="noopener">Staggered Differences-in-Differences</a></li> <li><a href="https://quarcs-lab.github.io/ccm/11-matrix-completion-and-ife.html" target="_blank" rel="noopener">Interactive Fixed Effects and Matrix Completion</a></li> <li><a href="https://quarcs-lab.github.io/ccm/12-gsynth.html" target="_blank" rel="noopener">Generalized Synthetic Control</a></li> </ol> <p>Plus: <a href="https://quarcs-lab.github.io/ccm/references.html" target="_blank" rel="noopener">References</a></p> <p>Contribute and provide feedback at <a href="https://github.com/quarcs-lab/ccm" target="_blank" rel="noopener">https://github.com/quarcs-lab/ccm</a>.</p> <h2 id="related-project">Related project</h2> <p>Companion resource: <a href="https://carlos-mendez.org/books/intro2causal/">Mastering Causal Metrics</a> — an AI-powered Python study guide based on Angrist &amp; Pischke&rsquo;s <em>Mastering &lsquo;Metrics</em>.</p>https://carlos-mendez.org/books/intro2causal/Wed, 22 Apr 2026 00:00:00 +0000https://carlos-mendez.org/books/intro2causal/<h2 id="welcome-to-mastering-causal-metrics">Welcome to Mastering Causal Metrics!</h2> <p>An AI-powered study guide to <strong>Mastering Causal Metrics</strong>. Learn the foundations of causal inference with interactive Python notebooks and AI tools, based on the foundational textbook <a href="https://www.masteringmetrics.com/" target="_blank" rel="noopener"><em>Mastering &lsquo;Metrics: The Path from Cause to Effect</em></a> by Angrist &amp; Pischke.</p> <p>This platform features:</p> <ul> <li><strong>Foundational Methods</strong> — Based on <em>Mastering &lsquo;Metrics</em> by Angrist &amp; Pischke. Learn causal inference from randomized trials to differences-in-differences.</li> <li><strong>Python Notebooks</strong> — Zero-installation Google Colab notebooks. Real datasets, working code, and complete implementations of every method.</li> <li><strong>AI-Powered Learning</strong> — Multiple AI tutors with distinct pedagogical styles.</li> </ul> <h2 id="interactive-google-colab-notebooks">Interactive Google Colab Notebooks</h2> <p>Click any badge below to open and run immediately in your browser:</p> <h3 id="part-i-the-framework">Part I: The Framework</h3> <table> <thead> <tr> <th>Chapter</th> <th>Title</th> <th>Topics</th> <th>Colab Notebook</th> </tr> </thead> <tbody> <tr> <td><strong>1</strong></td> <td>Randomized Trials</td> <td>Selection Bias, Potential Outcomes, RAND HIE</td> <td><a href="https://colab.research.google.com/github/cmg777/intro2causal/blob/main/notebooks_colab/01-randomized-trials.ipynb" target="_blank" rel="noopener"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td> </tr> </tbody> </table> <h3 id="part-ii-the-five-tools">Part II: The Five Tools</h3> <table> <thead> <tr> <th>Chapter</th> <th>Title</th> <th>Topics</th> <th>Colab Notebook</th> </tr> </thead> <tbody> <tr> <td><strong>2</strong></td> <td>Regression</td> <td>OLS, Omitted Variable Bias, Bad Controls</td> <td><a href="https://colab.research.google.com/github/cmg777/intro2causal/blob/main/notebooks_colab/02-regression.ipynb" target="_blank" rel="noopener"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td> </tr> <tr> <td><strong>3</strong></td> <td>Instrumental Variables</td> <td>LATE, Compliers, Minneapolis DV Experiment</td> <td><a href="https://colab.research.google.com/github/cmg777/intro2causal/blob/main/notebooks_colab/03-instrumental-variables.ipynb" target="_blank" rel="noopener"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td> </tr> <tr> <td><strong>4</strong></td> <td>Regression Discontinuity</td> <td>Sharp RD, Bandwidth, MLDA and Mortality</td> <td><a href="https://colab.research.google.com/github/cmg777/intro2causal/blob/main/notebooks_colab/04-regression-discontinuity.ipynb" target="_blank" rel="noopener"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td> </tr> <tr> <td><strong>5</strong></td> <td>Differences-in-Differences</td> <td>Parallel Trends, Two-Way FE, Great Depression Banking</td> <td><a href="https://colab.research.google.com/github/cmg777/intro2causal/blob/main/notebooks_colab/05-differences-in-differences.ipynb" target="_blank" rel="noopener"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td> </tr> </tbody> </table> <h3 id="part-iii-synthesis">Part III: Synthesis</h3> <table> <thead> <tr> <th>Chapter</th> <th>Title</th> <th>Topics</th> <th>Colab Notebook</th> </tr> </thead> <tbody> <tr> <td><strong>6</strong></td> <td>The Wages of Schooling</td> <td>Twins, Quarter of Birth, Sheepskin Effects</td> <td><a href="https://colab.research.google.com/github/cmg777/intro2causal/blob/main/notebooks_colab/06-wages-of-schooling.ipynb" target="_blank" rel="noopener"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td> </tr> </tbody> </table> <h3 id="how-to-use-the-notebooks">How to Use the Notebooks</h3> <ol> <li><strong>Click any &ldquo;Open in Colab&rdquo; badge</strong> above</li> <li><strong>Sign in</strong> with your Google account (free)</li> <li><strong>Click &ldquo;Run All&rdquo;</strong> in the Runtime menu (or run cells individually)</li> <li><strong>Explore and modify</strong> — change parameters, try different models, experiment with the data</li> <li><strong>Save your work</strong> — File &gt; Save a copy in Drive to keep your modifications</li> </ol> <p><strong>No installation, no downloads, no setup required!</strong></p> <h2 id="authors-and-credits">Authors and Credits</h2> <p><strong>Carlos Mendez</strong> — Python implementation and educational notebook development</p> <p><strong>Joshua D. Angrist &amp; Jörn-Steffen Pischke</strong> — Original textbook, <a href="https://www.masteringmetrics.com/" target="_blank" rel="noopener"><em>Mastering &lsquo;Metrics</em></a></p>https://carlos-mendez.org/books/metricsai/Mon, 26 Jan 2026 00:00:00 +0000https://carlos-mendez.org/books/metricsai/<h1 id="-econometrics-powered-by-ai">🤖 Econometrics Powered by AI</h1> <p><strong>An Introduction Using Cloud-based Python Notebooks</strong> <img src="https://raw.githubusercontent.com/quarcs-lab/metricsai/main/images/ch00_visual_summary.jpg" alt="Chapter 0 visual summary" style="max-width: 100%; height: auto;"></p> <hr> <h2 id="-vision">🚀 Vision</h2> <ul> <li>🤖 Econometrics in the AI era</li> <li>☁️ Cloud-based, interactive learning</li> <li>📊 Real data, real economic questions</li> </ul> <p>Learning econometrics is reframed as an active, computational, and AI-supported process that preserves theoretical rigor.</p> <hr> <h2 id="-why-rethink-econometrics-education">🧠 Why Rethink Econometrics Education?</h2> <ul> <li>📖 Passive textbooks</li> <li>💻 High technical barriers</li> <li>🔗 Gap between theory and implementation</li> </ul> <p>Traditional approaches often delay meaningful data analysis and hinder conceptual understanding.</p> <hr> <h2 id="-the-books-approach">🔄 The Book’s Approach</h2> <ul> <li>🧱 Foundational concepts</li> <li>🧪 Computational notebooks</li> <li>🤖 AI-powered learning</li> </ul> <p>A three-pillar system integrates theory, coding, and AI to create an active learning ecosystem.</p> <hr> <h2 id="-pillar-1-foundational-concepts">📘 Pillar 1: Foundational Concepts</h2> <ul> <li>📚 Based on Cameron (2022)</li> <li>📐 Rigorous econometric theory</li> <li>🌍 Applied, real-world focus</li> </ul> <p>The structure and content align with standard econometric practice and research.</p> <hr> <h2 id="-structure">🗂️ Structure</h2> <ul> <li>🧮 Statistical foundations</li> <li>📉 Bivariate regression</li> <li>📊 Multiple regression</li> <li>🔬 Advanced topics</li> </ul> <p>Seventeen chapters progress systematically from fundamentals to modern empirical methods.</p> <hr> <h2 id="-pillar-2-computational-notebooks">☁️ Pillar 2: Computational Notebooks</h2> <ul> <li>🚫 Zero installation</li> <li>🧑‍💻 Google Colab</li> <li>🌐 Access from any device</li> </ul> <p>Every chapter is paired with an interactive Python notebook.</p> <hr> <h2 id="-modern-python-stack">🧰 Modern Python Stack</h2> <ul> <li>🐼 Data manipulation</li> <li>📐 Econometric modeling</li> <li>📊 Visualization</li> </ul> <p>Students learn widely used, transferable tools for empirical research.</p> <hr> <h2 id="-learning-by-coding">🔁 Learning by Coding</h2> <ul> <li>✏️ Modify and rerun code</li> <li>⚡ Immediate feedback</li> <li>🔍 Active experimentation</li> </ul> <p>Understanding develops through direct interaction with data and models.</p> <hr> <h2 id="-pillar-3-ai-powered-learning">🤖 Pillar 3: AI-Powered Learning</h2> <ul> <li>🖼️ Visual summaries</li> <li>📑 AI-generated slides</li> <li>🎙️ Podcasts and quizzes</li> </ul> <p>Multiple modalities reinforce learning and accommodate diverse preferences.</p> <hr> <h2 id="-responsible-ai-use">⚖️ Responsible AI Use</h2> <ul> <li>🧠 AI supports, not replaces, thinking</li> <li>🔍 Verification is essential</li> <li>📘 Theory remains central</li> </ul> <p>AI enhances understanding but does not substitute for econometric reasoning.</p> <hr> <h2 id="-three-component-learning-system">🔗 Three-Component Learning System</h2> <ul> <li>📕 This book</li> <li>🌐 metricsAI website</li> <li>📘 Cameron’s textbook</li> </ul> <p>Together, they form a complete and coherent learning environment.</p> <hr> <h2 id="-conclusion">🎓 Conclusion</h2> <ul> <li>📊 Econometrics through computation</li> <li>🤖 Enhanced by AI</li> <li>🌍 Accessible and rigorous</li> </ul> <hr> <h2 id="-interactive-google-colab-notebooks">📓 Interactive Google Colab Notebooks</h2> <p>Click any badge below to open and run immediately in your browser:</p> <h3 id="part-i-statistical-foundations">Part I: Statistical Foundations</h3> <table> <thead> <tr> <th>Chapter</th> <th>Title</th> <th>Colab Notebook</th> </tr> </thead> <tbody> <tr> <td><strong>1</strong></td> <td>Analysis of Economics Data</td> <td><a href="https://colab.research.google.com/github/quarcs-lab/metricsai/blob/main/notebooks_colab/ch01_Analysis_of_Economics_Data.ipynb" target="_blank" rel="noopener"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td> </tr> <tr> <td><strong>2</strong></td> <td>Univariate Data Summary</td> <td><a href="https://colab.research.google.com/github/quarcs-lab/metricsai/blob/main/notebooks_colab/ch02_Univariate_Data_Summary.ipynb" target="_blank" rel="noopener"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td> </tr> <tr> <td><strong>3</strong></td> <td>The Sample Mean</td> <td><a href="https://colab.research.google.com/github/quarcs-lab/metricsai/blob/main/notebooks_colab/ch03_The_Sample_Mean.ipynb" target="_blank" rel="noopener"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td> </tr> <tr> <td><strong>4</strong></td> <td>Statistical Inference for the Mean</td> <td><a href="https://colab.research.google.com/github/quarcs-lab/metricsai/blob/main/notebooks_colab/ch04_Statistical_Inference_for_the_Mean.ipynb" target="_blank" rel="noopener"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td> </tr> </tbody> </table> <h3 id="part-ii-bivariate-regression">Part II: Bivariate Regression</h3> <table> <thead> <tr> <th>Chapter</th> <th>Title</th> <th>Colab Notebook</th> </tr> </thead> <tbody> <tr> <td><strong>5</strong></td> <td>Bivariate Data Summary</td> <td><a href="https://colab.research.google.com/github/quarcs-lab/metricsai/blob/main/notebooks_colab/ch05_Bivariate_Data_Summary.ipynb" target="_blank" rel="noopener"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td> </tr> <tr> <td><strong>6</strong></td> <td>The Least Squares Estimator</td> <td><a href="https://colab.research.google.com/github/quarcs-lab/metricsai/blob/main/notebooks_colab/ch06_The_Least_Squares_Estimator.ipynb" target="_blank" rel="noopener"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td> </tr> <tr> <td><strong>7</strong></td> <td>Statistical Inference for Bivariate Regression</td> <td><a href="https://colab.research.google.com/github/quarcs-lab/metricsai/blob/main/notebooks_colab/ch07_Statistical_Inference_for_Bivariate_Regression.ipynb" target="_blank" rel="noopener"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td> </tr> <tr> <td><strong>8</strong></td> <td>Case Studies for Bivariate Regression</td> <td><a href="https://colab.research.google.com/github/quarcs-lab/metricsai/blob/main/notebooks_colab/ch08_Case_Studies_for_Bivariate_Regression.ipynb" target="_blank" rel="noopener"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td> </tr> <tr> <td><strong>9</strong></td> <td>Models with Natural Logarithms</td> <td><a href="https://colab.research.google.com/github/quarcs-lab/metricsai/blob/main/notebooks_colab/ch09_Models_with_Natural_Logarithms.ipynb" target="_blank" rel="noopener"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td> </tr> </tbody> </table> <h3 id="part-iii-multiple-regression">Part III: Multiple Regression</h3> <table> <thead> <tr> <th>Chapter</th> <th>Title</th> <th>Colab Notebook</th> </tr> </thead> <tbody> <tr> <td><strong>10</strong></td> <td>Data Summary for Multiple Regression</td> <td><a href="https://colab.research.google.com/github/quarcs-lab/metricsai/blob/main/notebooks_colab/ch10_Data_Summary_for_Multiple_Regression.ipynb" target="_blank" rel="noopener"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td> </tr> <tr> <td><strong>11</strong></td> <td>Statistical Inference for Multiple Regression</td> <td><a href="https://colab.research.google.com/github/quarcs-lab/metricsai/blob/main/notebooks_colab/ch11_Statistical_Inference_for_Multiple_Regression.ipynb" target="_blank" rel="noopener"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td> </tr> <tr> <td><strong>12</strong></td> <td>Further Topics in Multiple Regression</td> <td><a href="https://colab.research.google.com/github/quarcs-lab/metricsai/blob/main/notebooks_colab/ch12_Further_Topics_in_Multiple_Regression.ipynb" target="_blank" rel="noopener"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td> </tr> <tr> <td><strong>13</strong></td> <td>Case Studies for Multiple Regression</td> <td><a href="https://colab.research.google.com/github/quarcs-lab/metricsai/blob/main/notebooks_colab/ch13_Case_Studies_for_Multiple_Regression.ipynb" target="_blank" rel="noopener"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td> </tr> </tbody> </table> <h3 id="part-iv-advanced-topics">Part IV: Advanced Topics</h3> <table> <thead> <tr> <th>Chapter</th> <th>Title</th> <th>Colab Notebook</th> </tr> </thead> <tbody> <tr> <td><strong>14</strong></td> <td>Regression with Indicator Variables</td> <td><a href="https://colab.research.google.com/github/quarcs-lab/metricsai/blob/main/notebooks_colab/ch14_Regression_with_Indicator_Variables.ipynb" target="_blank" rel="noopener"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td> </tr> <tr> <td><strong>15</strong></td> <td>Regression with Transformed Variables</td> <td><a href="https://colab.research.google.com/github/quarcs-lab/metricsai/blob/main/notebooks_colab/ch15_Regression_with_Transformed_Variables.ipynb" target="_blank" rel="noopener"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td> </tr> <tr> <td><strong>16</strong></td> <td>Checking the Model and Data</td> <td><a href="https://colab.research.google.com/github/quarcs-lab/metricsai/blob/main/notebooks_colab/ch16_Checking_the_Model_and_Data.ipynb" target="_blank" rel="noopener"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td> </tr> <tr> <td><strong>17</strong></td> <td>Panel Data, Time Series Data, Causation</td> <td><a href="https://colab.research.google.com/github/quarcs-lab/metricsai/blob/main/notebooks_colab/ch17_Panel_Data_Time_Series_Data_Causation.ipynb" target="_blank" rel="noopener"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td> </tr> </tbody> </table> <h3 id="how-to-use-the-notebooks">How to Use the Notebooks</h3> <ol> <li><strong>Click any &ldquo;Open in Colab&rdquo; badge</strong> above</li> <li><strong>Sign in</strong> with your Google account (free)</li> <li><strong>Click &ldquo;Run All&rdquo;</strong> in the Runtime menu (or run cells individually)</li> <li><strong>Explore and modify</strong> - change parameters, try different models, experiment with the data</li> <li><strong>Save your work</strong> - File → Save a copy in Drive to keep your modifications</li> </ol> <p><strong>No installation, no downloads, no setup required!</strong></p> <h2 id="-authors-and-credits">👥 Authors and Credits</h2> <p><strong>Carlos Mendez</strong> - Python implementation and educational notebook development</p> <p><strong>A. Colin Cameron</strong> - Original textbook, data, Stata/R code, slides.</p>https://carlos-mendez.org/books/convergence-clubs/Tue, 17 Nov 2020 00:00:00 +0000https://carlos-mendez.org/books/convergence-clubs/https://carlos-mendez.org/books/essays-productivity/Mon, 27 Jul 2015 00:00:00 +0000https://carlos-mendez.org/books/essays-productivity/